python install pytorch cuda
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PyTorch is a popular deep learning library for Python, and CUDA is a parallel computing platform and application programming interface model created by Nvidia. When you install PyTorch with CUDA support, you enable GPU acceleration for your deep learning tasks, significantly improving performance. This tutorial will guide you through the process of installing PyTorch with CUDA on a Linux system.
Before you begin, make sure that your GPU is compatible with CUDA. You can check the CUDA GPU support list to verify if your GPU is supported.
Ensure that you have the latest Nvidia GPU drivers installed on your system. You can download the drivers from the Nvidia website.
Download and install the CUDA Toolkit. The version of the toolkit should be compatible with the version of PyTorch you intend to install. You can find the recommended CUDA version in the PyTorch documentation.
Replace VERSION, MAJOR, MINOR, and PATCH with the appropriate values.
Add the following lines to your shell configuration file (e.g., ~/.bashrc or ~/.zshrc) to set the necessary environment variables:
Source the updated configuration:
Now, you can install PyTorch with CUDA support using pip. Make sure to check the official PyTorch website for the latest installation command.
Replace VERSION with the appropriate CUDA version.
You can verify that PyTorch is using the GPU by running the following code in a Python script or interactive environment:
If everything is set up correctly, you should see True for torch.cuda.is_available() and information about your GPU.
Congratulations! You have successfully installed PyTorch with CUDA support on your system. You can now leverage the power of GPU acceleration for your deep learning projects.
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